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"Pandemi COVID-19 mendorong adanya transformasi kesehatan, terutama dalam praktik kedokteran gigi. Respon terhadap risiko penularan menggiring masyarakat menuju layanan telemedicine, khususnya teledentistry. Fenomena ini menciptakan paradigma baru dalam ortodonti, mendorong perkembangan teleorthodontic. Dukungan teknologi machine learning di bidang ortodonti menawarkan solusi inovatif untuk diagnosis dini dan peningkatan aksesibilitas layanan ortodontik. Penelitian ini akan membandingkan 3 model computer vision yaitu EfficientNet, MobileNet, dan ShuffleNet disertai dengan adanya penambahan model tabular yaitu TabNet. Implementasi model computer vision ini bertujuan untuk dapat memberikan analisis awal bagi pasien ortodonti dan akan dievaluasi menggunakan metrik F1-score dan interpretability ahli dengan bantuan LIME. Berdasarkan penelitian ini, ditemukan bahwa model computer vision ShuffleNet memiliki rata-rata hasil nilai F1-score terbaik diikuti dengan EfficientNet dan terakhir MobileNet. Perbedaan nilai tersebut berkisar antara 1-5% antara EfficientNet dan ShuffleNet namun perbedaan melebar untuk MobileNet dan ShuffleNet yang berkisar antara 3-8%. Selain itu, penambahan TabNet dalam framework memberikan peningkatan rata-rata nilai F1-score sebesar 2.7% hingga 5% dibandingkan model yang tidak menggunakan TabNet.

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The COVID-19 pandemic has driven health transformation, especially in dental practice. The response to the risk of transmission leads the public towards telemedicine services, especially teledentistry. This phenomenon creates a new paradigm in orthodontics, encouraging the development of teleorthodontics. The support of machine learning technology in orthodontics offers innovative solutions for early diagnosis and increased accessibility to orthodontic services. This study will compare 3 computer vision models, which are EfficientNet, MobileNet, and ShuffleNet, accompanied by adding a tabular model, which is TabNet. The implementation of this computer vision model aims to provide an initial analysis for orthodontic patients and will be evaluated using the F1-score metric and expert interpretability with the help of LIME. This study found that the ShuffleNet computer vision model has the best average F1-score, followed by EfficientNet, and finally MobileNet. The difference in value ranges between 1-5% between EfficientNet and ShuffleNet, but the difference widens for MobileNet and ShuffleNet, which ranges between 3-8%. In addition, adding TabNet to the framework provides an average increase in F1-score by 2.7% to 5% compared to models that do not use TabNet."

"Pandemi COVID-19 mendorong adanya transformasi kesehatan, terutama dalam praktik kedokteran gigi. Respon terhadap risiko penularan menggiring masyarakat menuju layanan telemedicine, khususnya teledentistry. Fenomena ini menciptakan paradigma baru dalam ortodonti, mendorong perkembangan teleorthodontic. Dukungan teknologi machine learning di bidang ortodonti menawarkan solusi inovatif untuk diagnosis dini dan peningkatan aksesibilitas layanan ortodontik. Penelitian ini akan membandingkan 3 model computer vision yaitu EfficientNet, MobileNet, dan ShuffleNet disertai dengan adanya penambahan model tabular yaitu TabNet. Implementasi model computer vision ini bertujuan untuk dapat memberikan analisis awal bagi pasien ortodonti dan akan dievaluasi menggunakan metrik F1-score dan interpretability ahli dengan bantuan LIME. Berdasarkan penelitian ini, ditemukan bahwa model computer vision ShuffleNet memiliki rata-rata hasil nilai F1-score terbaik diikuti dengan EfficientNet dan terakhir MobileNet. Perbedaan nilai tersebut berkisar antara 1-5% antara EfficientNet dan ShuffleNet namun perbedaan melebar untuk MobileNet dan ShuffleNet yang berkisar antara 3-8%. Selain itu, penambahan TabNet dalam framework memberikan peningkatan rata-rata nilai F1-score sebesar 2.7% hingga 5% dibandingkan model yang tidak menggunakan TabNet.

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The COVID-19 pandemic has driven health transformation, especially in dental practice. The response to the risk of transmission leads the public towards telemedicine services, especially teledentistry. This phenomenon creates a new paradigm in orthodontics, encouraging the development of teleorthodontics. The support of machine learning technology in orthodontics offers innovative solutions for early diagnosis and increased accessibility to orthodontic services. This study will compare 3 computer vision models, which are EfficientNet, MobileNet, and ShuffleNet, accompanied by adding a tabular model, which is TabNet. The implementation of this computer vision model aims to provide an initial analysis for orthodontic patients and will be evaluated using the F1-score metric and expert interpretability with the help of LIME. This study found that the ShuffleNet computer vision model has the best average F1-score, followed by EfficientNet, and finally MobileNet. The difference in value ranges between 1-5% between EfficientNet and ShuffleNet, but the difference widens for MobileNet and ShuffleNet, which ranges between 3-8%. In addition, adding TabNet to the framework provides an average increase in F1-score by 2.7% to 5% compared to models that do not use TabNet."

"Tujuan: Menganalisis perbedaan Indeks Gingiva antara penggunaan sikat gigi ortodonti dan sikat gigi non-ortodonti pada pasien yang dirawat dengan piranti ortodonti cekat. Metode: Penelitian eksperimental klinis, blinded-examiner dengan 32 (tiga puluh dua) subjek yang dibagi secara acak menjadi dua kelompok sikat gigi. Dilakukan pemeriksaan Indeks Gingiva Löe dan Silness pada gigi 16, 21, 25, 36, 41, dan 45 sebelum perlakuan dan tiga minggu setelah perlakuan. Hasil: Tidak terdapat perbedaan Indeks Gingiva yang bermakna antara penggunaan sikat gigi ortodonti dan sikat gigi non-ortodonti pada pasien perawatan ortodonti cekat (uji Mann-Whitney; p>0,05). Kesimpulan: Penggunaan sikat gigi ortodonti maupun sikat gigi non-ortodonti, keduanya dapat menurunkan Indeks Gingiva pada pasien perawatan ortodonti cekat.

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Objective: To analyze the differences of Gingival Index between usage of orthodontic toothbrushes and non-orthodontic toothbrushes in fixed orthodontic patients.

Method: The study is clinical experimental with blinded examiner. Thirty-two subjects were randomly divided into two groups of toothbrushes. Examinations were done using Gingival Index Löe and Silness on teeth 16, 21, 25, 36, 41, 45 before experiment and three weeks after experiment.

Result: There was no significant difference in Gingival Index between usage of orthodontic toothbrushes and non-orthodontic toothbrushes in fixed orthodontic patients (Mann-Whitney test; p>0,05).

Conclusion: Usage of orthodontic toothbrush and non-orthodontic toothbrush both can reduce Gingival Index in fixed orthodontic patients."

"Latar Belakang: Kebersihan mulut yang baik dibutuhkan oleh pasien yang dirawat dengan alat ortodonti cekat, karena adanya alat-alat ortodonti seperti brackets, arch wire, bands, ligatures dan auxaillaries dapat memudahkan plak dan debris terkumpul di sekitarnya. Salah satu cara kontrol plak gigi yang paling umum ialah dengan menyikat gigi. Tujuan: Menganalisis perbedaan indeks plak antara penggunaan sikat gigi ortodonti dan sikat gigi konvensional pada pasien yang dirawat dengan alat ortodonti cekat. Metode: Pada penelitian eksperimental klinis ini, 32 (tiga puluh dua) subjek yang dibagi secara acak ke dalam dua kelompok yaitu kelompok sikat gigi ortodonti dan kelompok sikat gigi konvensional. Subjek diberikan pasta gigi yang sama dan diinstruksikan untuk menyikat gigi dua kali sehari dengan metode Bass selama dua menit. Skor Indeks Plak diukur sebelum dan sesudah penggunaan sikat gigi selama tiga minggu berturut-turut. Hasil: Hasil uji Mann-Whitney menyimpulkan tidak terdapat perbedaan bermakna secara statistik antara penggunaan sikat gigi ortodonti dan sikat gigi konvensional pada pasien perawatan ortodonti cekat (p>0,05). Kesimpulan: Penggunaan sikat gigi ortodonti maupun sikat gigi konvensional sama-sama efektif menurunkan indeks plak pada pasien yang dirawat dengan alat ortodonti cekat.

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Background: Patients with fixed orthodontic appliances need to maintain good oral hygiene because the presence of orthodontic appliances such as brackets, arch wire, bands, ligatures and auxaillaries can facilitate plaque and debris accumulation around those sites. The most common way to control dental plaque is by toothbrushing.

Aim: To analyze plaque index differences between the use of orthodontic toothbrush and conventional toothbrush in patients with fixed orthodontic treatment.

Method: In this clinical experimental study, thirty two subjects were randomly divided into two groups which are orthodontic toothbrush group and conventional toothbrush group. Subjects were given the same toothpaste and instructed to brush their teeth twice a day with Bass method for two minutes. Plaque index scores were measured before and after three consecutive weeks of toothbrush usage.

Result: The results of Mann-Whitney test concludes that there is no statistically significant difference between the use of orthodontic toothbrush and conventional toothbrush in patients with fixed orthodontic appliances (p>0,05).

Conclusion: The use of orthodontic toothbrush and conventional toothbrush equally effective to decrease plaque index in patients with fixed orthodontic appliances."

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Supermarket merupakan tempat pilihan terbaik untuk berbelanja kebutuhan rumah saat ini karena pelanggan dapat memilih produk yang ingin dibelinya tanpa perlu mengantre. Namun untuk melakukan pembayaran saat ini pelanggan masih perlu mengantre di kasir. Oleh karena itu, penelitian ini akan mengimplementasikan sistem cashierless yang dapat melakukan checkout secara otomatis dan efisien sehingga pelanggan tidak perlu mengantre lagi di kasir. Sistem cashierless yang digunakan pada penelitian ini adalah smart trolley, sistem ini dapat melakukan deteksi produk yang masuk atau keluar dari troli pelanggan lalu melakukan checkout secara otomatis saat pelanggan keluar dari supermarket. Untuk dapat melakukan deteksi produk diperlukan model machine learning yang berjenis object detection. Model juga harus dapat diimplementasikan pada edge device karena deteksi akan dilakukan di troli yang memiliki keterbatasan ruang. Maka model yang digunakan adalah YOLOv5 karena memiliki akurasi serta performa tinggi supaya tetap dapat diimplementasikan pada edge device. Hasil pengujian variasi backbone menunjukkan backbone original lebih baik dari backbone Swin Transformer dengan nilai F1-Score sebesar 98.64%, ukuran model sebesar 7.7 MB, dan dapat berjalan dengan 3.87 FPS di komputer pengujian dan 0.74 FPS di Raspberry Pi 4B. Hasil pengujian variasi dataset menunjukkan kombinasi dataset bergerak dengan statis blur dapat menghasilkan model yang memiliki akurasi yang paling baik dengan nilai 99.53% pada fase pelatihan dan 99.44% pada fase testing. Hasil pengujian intensitas cahaya menunjukkan penggunaan lampu untuk meningkatkan pencahayaan di sekitar wilayah deteksi di dalam troli dapat meningkatkan F1-Score hasil deteksi yang dilakukan hingga 63.55%. Hasil pengujian variasi kecepatan produk menunjukkan kecepatan ideal yang dapat digunakan pada saat proses deteksi di komputer pengujian adalah hingga 36 cm/s dan untuk proses yang dilakukan di Raspberry Pi 4B adalah di bawah 7 cm/s. Hasil pengujian dengan penambahan sampling rate dapat mendeteksi produk di komputer pengujian dengan kecepatan hingga 124 cm/s pada produk-produk dengan ukuran yang cukup lebar.

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Supermarkets are the best place to shop for home needs today because customers can choose what products they want to buy without the need to queue. However, today customers still need to queue at the cashier to make payments. Therefore, this research will implement a cashier-less system that can do checkout automatically and efficiently so that customers don't have to queue at the cashier anymore. The cashier-less system used in this study is a smart trolley, this system can detect products entering or leaving the customer's trolley and then checkout automatically when the customer leaves the supermarket. To be able to perform product detection, a machine learning model of the object detection type is needed. The model must be able implemented on edge devices because the detection will be done in the cart with limited space. So, the model used is YOLOv5 because it has high accuracy and performance so it can implement on edge devices. The backbone variation test results show that the original backbone is better than the Swin-Transformer backbone with an F1-Score value of 98.64%, a model size of 7.7 MB, and can run with 3.87 FPS on a test computer and 0.74 FPS on a Raspberry Pi 4B. The dataset variation test results show that the combination of moving datasets with static blur can produce a model with the best accuracy of 99.53% in the training phase and 99.44% in the testing phase. The light intensity variation test results show that the use of lamps to increase the lighting around the detection area in the trolley can increase the F1-Score of the detection results made up to 63.55%. The product speed variation results show that the ideal speed that can use during the detection process on the testing computer is up to 36 cm/s and for the process carried out on the Raspberry Pi 4B it is below 7 cm/s. The sampling rate addition results can detect products on the test computer at speeds up to 124 cm/s on products with a wide size

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"Ekstraksi premolar dalam perawatan ortodonti membantu proses uprighting gigi molar 3 impaksi sehingga dapat erupsi dengan baik. Tujuan: mengukur perubahan angulasi gigi molar 3 rahang bawah yang impaksi mesioangular sebelum dan sesudah perawatan ortodonti. Metode: penelitian ini menggunakan 25 radiograf panoramik berusia 10-21 tahun sebelum dan sesudah perawatan ortodonti. Hasil: uji Wilcoxon dan uji T berpasangan (p<0,05) menunjukkan tidak ada perubahan angulasi molar 3 yang bermakna pada kedua sisi (p>0,05) dan cenderung mengalami peningkatan angulasi dengan meskipun secara statistik perbandingan perubahan keduanya tidak berbeda bermakna (p>0,05). Peningkatan angulasi paling banyak terjadi pada kelompok usia dewasa (17-21 tahun). Kesimpulan: ekstraksi premolar dalam perawatan ortodonti tidak memengaruhi angulasi gigi molar 3 impaksi secara bermakna.

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Premolar extraction in orthodontic treatment helps uprighting process of impacted third molars so that they could erupt well.

Aim: to measure mesioangular impacted lower third molars angulation change during orthodontic treatment.

Methods: this study used 25 panoramic radiograph aged 10-21 years old before and after orthodontic treatment.

Result: Wilcoxon test and paired Ttest (p<0,05) showed there were no significant change in lower third molars angulation on both sides (p>0,05) and tended to experience the increase in angulation though statistically comparison between them were not significant (p>0,05). These increase happen the most in the adult group (17-21 years old).

Conclusion: premolars extraction in orthodontic treatment does not affect impacted third molars angulation significantly."

"Diagnosis COVID-19 dapat dilakukan dengan berbagai metode, salah satunya dengan interpretasi citra medis rongga dada menggunakan machine learning. Namun, metode ini memiliki memerlukan waktu dan biaya yang besar, tidak ada standar dalam pengambilan gambar citra medis, dan pelindungan privasi pada data pasien. Model yang dilatih dengan dataset publik tidak selalu dapat mempertahankan performanya. Diperlukan metode pengklasifikasi berbasis multicenter yang dapat memiliki performa optimal pada dataset yang berbeda-beda. Skenario pertama dengan melatih model menggunakan arsitektur VGG-19 dan ConvNeXt dengan gabungan seluruh data dan masing-masing data. Lalu dilakukan fine tuning terhadap model yang dilatih pada gabungan seluruh data. Skenario kedua dengan Unsupervised Domain Adaptation berbasis maximum mean discrepancy dengan data publik sebagai source domain dan data privat sebagai target domain. Metode transfer learning dengan fine-tuning model pada arsitektur VGG-19 menaikkan train accuracy pada data Github menjadi 95% serta menaikkan test accuracy pada data Github menjadi 93%, pada data Github menjadi 93%, pada data RSCM menjadi 72%, dan pada data RSUI menjadi 75%. Metode transfer learning dengan fine-tuning model pada arsitektur ConvNeXt menaikkan evaluation accuracy pada data RSCM menjadi 73%. Metode unsupervised domain adaptation (UDA) berbasis maximum mean discrepancy (MMD) memiliki akurasi sebesar 89% pada dataset privat sehingga merupakan metode yang paling baik. Berdasarkan GRAD-CAM, model sudah mampu mendeteksi bagian paru-paru dari citra X-Ray dalam memprediksi kelas yang sesuai.

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Diagnosis of COVID-19 can be done using various methods, one of which is by interpreting medical images of the chest using machine learning. However, this method requires a lot of time and money, there is no standard in taking medical images, and protecting patient data privacy. Models that are trained with public datasets do not always maintain their performance. A multicenter-based classification method is needed that can have optimal performance on different datasets. The first scenario is to train the model using the VGG-19 and ConvNeXt architecture by combining all data and each data. Then, the model trained using combined data is fine tuned. The second scenario uses Unsupervised Domain Adaptation based on maximum mean discrepancy with public data as the source domain and private data as the target domain. The transfer learning method with the fine-tuning model on the VGG-19 architecture increases train accuracy on Github data to 95% and increases test accuracy on Github data to 93%, on Github data to 93%, on RSCM data to 72%, and on data RSUI to 75%. The transfer learning method with the fine-tuning model on the ConvNeXt architecture increases the evaluation accuracy of RSCM data to 73%. The unsupervised domain adaptation (UDA) method based on maximum mean discrepancy (MMD) has an accuracy of 89% in private dataset making it the best method. Based on GRAD-CAM, the model has been able to detect parts of the lungs from X-Ray images in predicting the appropriate class."

"Objectives: (1) To assess the masticatory muscles activity in patients with Temporomandibular Disorder (TMD) before orthodontic treatment, (2) to determine the correlation between TMD and the masticatory muscles activity (masseter muscles and anterior temporalis muscles). Methods: Twenty-two patients with malocclusion before undergoing orthodontic treatment (8 males, 14 females; mean age of 26,78 ± 4.34 years) were enrolled in the study and were divided into two groups: 11 patients with TMD and 11 patients without TMD (Non- TMD). The masticatory muscles were evaluated using standardized electromyography during 5 seconds of maximum voluntary contraction (MVC) through cotton-roll biting. For statistical analysis, the root mean square (RMS) valueof masticatory muscles was calculated and compared between the two groups. Results: The TMD groups showed alower electromyographic activity than the non- TMD group during MVC, with no significant differences in the right and left masticatory muscles between these groups. A weak negative correlation and no statistically significant differences were found between TMD and the electromyography activity of masseter muscles. Conclusions: Patients with TMD had a lower electromyographic activity in the masticatory muscles than those without TMD. Thus, electromyography can be an objective parameter to assess muscles activity for TMDdiagnosis.

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Objectives: (1) To assess the masticatory muscles activity in patients with Temporomandibular Disorder (TMD) before orthodontic treatment, (2) to determine the correlation between TMD and the masticatory muscles activity (masseter muscles and anterior temporalis muscles). Methods: Twenty-two patients with malocclusion before undergoing orthodontic treatment (8 males, 14 females; mean age of 26,78 ± 4.34 years) were enrolled in the study and were divided into two groups: 11 patients with TMD and 11 patients without TMD (Non- TMD). The masticatory muscles were evaluated using standardized electromyography during 5 seconds of maximum voluntary contraction (MVC) through cotton-roll biting. For statistical analysis, the root mean square (RMS) valueof masticatory muscles was calculated and compared between the two groups. Results: The TMD groups showed alower electromyographic activity than the non- TMD group during MVC, with no significant differences in the right and left masticatory muscles between these groups. A weak negative correlation and no statistically significant differences were found between TMD and the electromyography activity of masseter muscles. Conclusions: Patients with TMD had a lower electromyographic activity in the masticatory muscles than those without TMD. Thus, electromyography can be an objective parameter to assess muscles activity for TMDdiagnosis."

"Kompleksitas maloklusi seperti ketidakteraturan gigi anterior menjadi salah satu hal penting dalam menentukan hasil perawatan dengan alat ortodonti lepas. Indeks iregularitas Little merupakan indeks yang digunakan untuk menilai perubahan susunan gigi anterior. Tujuan: untuk mengetahui gambaran kompleksitas maloklusi terutama ketidakteraturan gigi anterior dan hasil perawatan dengan alat ortodonti lepas di Klinik Integrasi RSKGM FKG UI menggunakan indeks iregularitas Little. Metode: Penelitian ini merupakan penelitian deskriptif dengan sampel berupa 47 cetakan model gigi pasien sebelum dan setelah perawatan dengan alat ortodonti lepas di Klinik Integrasi RSKGM FKG UI yang dirawat dalam periode 2013-2017 diukur menggunakan indeks iregularitas Little. Hasil: Pasien yang paling banyak datang untuk melakukan perawatan dengan alat ortodonti lepas memiliki kondisi ketidakteraturan gigi anterior berupa ketidakteraturan minimal dan ketidakteraturan sedang, setelah dilakukan perawatan terdapat perubahan kondisi gigi anterior pasien menjadi tidak ada ketidakteraturan dan ketidakteraturan minimal serta tidak ditemukan lagi pasien dengan kondisi ketidakteraturan berat. Kesimpulan: Terdapat perbaikan kondisi gigi anterior pasien pada rahang atas dan rahang bawah setelah dilakukan perawatan dengan alat ortodonti lepas yang dilakukan oleh mahasiswa profesi di Klinik Integrasi RSKGM FKG UI tahun 2013-2017, sehingga perawatan dapat dinyatakan baik dan sesuai dengan indikasi perawatan serta fungsi alat ortodonti lepas.

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The complexity of malocclusion such as anterior teeth irregularity had become one of the important things to determine the outcome of removable orthodontic appliance treatment. Little's irregularity index is an index used to assess the change of anterior teeth alignment.

Aim: To determine the complexity of malocclusion especially the irregularity of anterior teeth and the outcome of removable orthodontic appliance treatment at RSKGM FKG UI Integration Clinic patients using the Little's irregularity index.

Method: This study is a descriptive study with a sample of 47 pretreatment and post treatment patient's study model at RSKGM FKG UI Integration Clinic patients which are treated within the period 2013 2017 measured using Little's Irregularity Index.

Result: Most patients who came to seek treatment using a removable orthodontic appliance had an anterior teeth irregularity of minimal and moderate irregularity, and there were changes in anterior teeth region after treatment to no irregularity and minimal irregularity and none of the patients with severe irregularity.

Conclusion: There's improvement of the anterior teeth condition of the patient on the maxilla and mandible jaw after treatment with removable orthodontic appliance performed by clinical students at RSKGM FKG UI Integration Clinic in 2013 2017, so that the treatment can be stated good and in accordance with the indication of treatment and the function of removable orthodontic appliance."

"Indonesia merupakan negara maritim terbesar di dunia dengan banyak sekali ikan yang hidup di perairan Indonesia Hal ini membuat sektor perikanan Indonesia memiliki banyak ancaman. Illegal, unreported, unregulated (IUU) fishing adalah salah satu permasalahan yang memiliki dampak yang cukup signifikan karena membuat kerugian yang cukup besar di sektor perikanan Indonesia. Untuk mencegah permasalahan tersebut, sudah banyak solusi yang diajukan, salah satunya adalah penerapan kuota untuk operasi penangkapan ikan serta pemasangan kamera pengawas, namun solusi tersebut belum memiliki dampak yang signifikan dalam mengurangi dan mencegah terjadinya IUU fishing. Oleh karena itu, penelitian ini dilakukan untuk mengembangkan sistem deteksi jenis ikan hasil tangkapan. Sistem dirancang menggunakan konsep object detection dan instance segmentation yang merupakan sebuah bidang dari machine learning, menggunakan toolbox MMDetection dengan algoritma Faster R-CNN dan GFL untuk metode object detection dan algoritma Mask R-CNN untuk metode instance segmentation. Dimana sistem tersebut merupakan model kecerdasan buatan yang dapat melakukan pendeteksian ikan untuk melakukan pengawasan terhadap jumlah ikan yang ditangkap oleh nelayan sehingga IUU fishing dapat berkurang secara signifikan. Sistem terbaik dari penelitian ini dihasilkan menggunakan model instance segmentation yang mendapatkan nilai mAP @50 0,758, besar F1-Score 0,761, dan membutuhkan waktu untuk pelatihan selama 7 jam 32 menit. Selain itu, model tersebut juga mendapatkan akurasi yang lebih baik sebanyak 20% dari perbandingan dengan model object detection.

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Indonesia, as the world's largest maritime country, is home to a vast variety of fish species in its waters. This reality poses numerous threats to Indonesia's fisheries sector. One significant challenge is illegal, unreported, and unregulated (IUU) fishing, which has considerable detrimental effects and causes substantial losses to the Indonesian fisheries industry. Several solutions have been proposed to address this problem, including the implementation of fishing quotas and the installation of surveillance cameras. However, these solutions have not yielded significant impacts in reducing and preventing IUU fishing. Hence, this research aims to develop a fish species detection system. The system is designed based on the concepts of object detection and instance segmentation, which are subfields of machine learning. The research utilizes the MMDetection toolbox with the Faster R-CNN and GFL algorithms for object detection, as well as the Mask R-CNN algorithm for instance segmentation. This artificial intelligence-based system enables the detection of captured fish to monitor the quantity of fish caught by fishermen, thereby significantly reducing IUU fishing. The research's best-performing system employs the instance segmentation model, achieving an mAP@50 score of 0.758, an F1-Score of 0.761, and requires a training time of 7 hours and 32 minutes. Moreover, this model also demonstrates a 20% improvement in accuracy compared to the object detection model."